Bug report: incorrect parameter assignment in "VAE_Standard_MLP_decoder" initialization

[HaolingZHANG]

Description:

In the VAE_Standard_MLP_decoder class, the __init__ funciton assigns parameters using params, but the method does not take params as an argument. Instead, it expects multiple independent parameters. This leads to a KeyError or TypeError when executing the code.

Bug Location:

Start from https://github.com/OATML/EVE/blob/master/EVE/VAE_decoder.py#L173, the screenshot is:

def __init__(self, seq_len, alphabet_size, hidden_layers_sizes, z_dim, first_hidden_nonlinearity, last_hidden_nonlinearity, dropout_proba,
             convolve_output, convolution_depth, include_temperature_scaler, include_sparsity, num_tiles_sparsity):
    """
    Required input parameters:
    - seq_len: (Int) Sequence length of sequence alignment
    - alphabet_size: (Int) Alphabet size of sequence alignment (will be driven by the data helper object)
    - hidden_layers_sizes: (List) List of the sizes of the hidden layers (all DNNs)
    - z_dim: (Int) Dimension of latent space
    - first_hidden_nonlinearity: (Str) Type of non-linear activation applied on the first (set of) hidden layer(s)
    - last_hidden_nonlinearity: (Str) Type of non-linear activation applied on the very last hidden layer (pre-sparsity)
    - dropout_proba: (Float) Dropout probability applied on all hidden layers. If 0.0 then no dropout applied
    - convolve_output: (Bool) Whether to perform 1d convolution on output (kernel size 1, stide 1)
    - convolution_depth: (Int) Size of the 1D-convolution on output
    - include_temperature_scaler: (Bool) Whether we apply the global temperature scaler
    - include_sparsity: (Bool) Whether we use the sparsity inducing scheme on the output from the last hidden layer
    - num_tiles_sparsity: (Int) Number of tiles to use in the sparsity inducing scheme (the more the tiles, the stronger the sparsity)
    - bayesian_decoder: (Bool) Whether the decoder is bayesian or not
    """
    super().__init__()
    self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    self.seq_len = params['seq_len']
    self.alphabet_size = params['alphabet_size']
    self.hidden_layers_sizes = params['hidden_layers_sizes']
    self.z_dim = params['z_dim']
    self.bayesian_decoder = False
    self.dropout_proba = params['dropout_proba']
    self.convolve_output = params['convolve_output']
    self.convolution_depth = params['convolution_depth']
    self.include_temperature_scaler = params['include_temperature_scaler']
    self.include_sparsity = params['include_sparsity']
    self.num_tiles_sparsity = params['num_tiles_sparsity']

    self.mu_bias_init = 0.1

    self.hidden_layers=nn.ModuleDict()
    for layer_index in range(len(self.hidden_layers_sizes)):
        if layer_index==0:
            self.hidden_layers[str(layer_index)] = nn.Linear(self.z_dim, self.hidden_layers_sizes[layer_index])
            nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init)
        else:
            self.hidden_layers[str(layer_index)] = nn.Linear(self.hidden_layers_sizes[layer_index-1],self.hidden_layers_sizes[layer_index])
            nn.init.constant_(self.hidden_layers[str(layer_index)].bias, self.mu_bias_init)

    if params['first_hidden_nonlinearity'] == 'relu':
        self.first_hidden_nonlinearity = nn.ReLU()
    elif params['first_hidden_nonlinearity'] == 'tanh':
        self.first_hidden_nonlinearity = nn.Tanh()
    elif params['first_hidden_nonlinearity'] == 'sigmoid':
        self.first_hidden_nonlinearity = nn.Sigmoid()
    elif params['first_hidden_nonlinearity'] == 'elu':
        self.first_hidden_nonlinearity = nn.ELU()
    elif params['first_hidden_nonlinearity'] == 'linear':
        self.first_hidden_nonlinearity = nn.Identity()
    
    if params['last_hidden_nonlinearity'] == 'relu':
        self.last_hidden_nonlinearity = nn.ReLU()
    elif params['last_hidden_nonlinearity'] == 'tanh':
        self.last_hidden_nonlinearity = nn.Tanh()
    elif params['last_hidden_nonlinearity'] == 'sigmoid':
        self.last_hidden_nonlinearity = nn.Sigmoid()
    elif params['last_hidden_nonlinearity'] == 'elu':
        self.last_hidden_nonlinearity = nn.ELU()
    elif params['last_hidden_nonlinearity'] == 'linear':
        self.last_hidden_nonlinearity = nn.Identity()

    if self.dropout_proba > 0.0:
        self.dropout_layer = nn.Dropout(p=self.dropout_proba)

    if self.convolve_output:
        self.output_convolution = nn.Conv1d(in_channels=self.convolution_depth,out_channels=self.alphabet_size,kernel_size=1,stride=1,bias=False)
        self.channel_size = self.convolution_depth
    else:
        self.channel_size = self.alphabet_size
    
    if self.include_sparsity:
        self.sparsity_weight = nn.Parameter(torch.randn(int(self.hidden_layers_sizes[-1]/self.num_tiles_sparsity), self.seq_len))

    self.W_out = nn.Parameter(torch.zeros(self.channel_size * self.seq_len,self.hidden_layers_sizes[-1]))
    nn.init.xavier_normal_(self.W_out) #Initialize weights with Glorot initialization
    self.b_out = nn.Parameter(torch.zeros(self.alphabet_size * self.seq_len))
    nn.init.constant_(self.b_out, self.mu_bias_init)
    
    if self.include_temperature_scaler:
        self.temperature_scaler = nn.Parameter(torch.ones(1))